{"product_id":"bias-and-causation-9780470286395","title":"Bias and Causation","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eA one-of-a-kind resource on identifying and dealing with bias in statistical research on causal effects\u003c\/b\u003e  \u003cp\u003eDo cell phones cause cancer? Can a new curriculum increase student achievement? Determining what the real causes of such problems are, and how powerful their effects may be, are central issues in research across various fields of study. Some researchers are highly skeptical of drawing causal conclusions except in tightly controlled randomized experiments, while others discount the threats posed by different sources of bias, even in less rigorous observational studies. Bias and Causation presents a complete treatment of the subject, organizing and clarifying the diverse types of biases into a conceptual framework. The book treats various sources of bias in comparative studiesboth randomized and observationaland offers guidance on how they should be addressed by researchers.\u003c\/p\u003e \u003cp\u003eUtilizing a relatively simple mathematical approach, the author develops a theory of bias that\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\"The book combines a useful synthesis of the literature with an original working through of issues related to bias and causal inference. Anyone with a sustained interest in this topic will find the book worth reading.\" (\u003ci\u003eJournal of Educational and Behavioral Statistics\u003c\/i\u003e, May 2012)\u003c\/p\u003e \u003cp\u003e\"...the book provides a unified framework for understanding issues of causal inference discussed differently across disciplines...the book will also be of substantial interest to methodologically minded readers working within specific disciplines but interested in methodological literature from other disciplines.\" (\u003ci\u003eJournal of Educational and Behavioral Statistics\u003c\/i\u003e, May 2012)\u003c\/p\u003e \u003cp\u003e\"The book covers almost all the relevant biases that can be present when designing and analyzing treatment effects in comparative studies.\" (\u003ci\u003eJournal of Biopharmaceutical Statistics\u003c\/i\u003e, January 2011)\"A consultant who specializes in applying statistics to various business and legal issues, Weisberg explains approaches to bias and causal inference, a realm statisticians have avoided until recently because it requires intuitive skills beyond the pale of mathematics. He writes for practicing researchers and methodologists and for students with a reasonably solid grounding in basic statistics and research methods.\" (\u003ci\u003eSciTech Book News,\u003c\/i\u003e December 2010)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. What Is Bias? 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Apples and Oranges, 2\u003c\/p\u003e \u003cp\u003e1.2 Statistics vs. Causation, 3\u003c\/p\u003e \u003cp\u003e1.3 Bias in the Real World, 6\u003c\/p\u003e \u003cp\u003eGuidepost 1, 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Causality and Comparative Studies 24\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Bias and Causation, 24\u003c\/p\u003e \u003cp\u003e2.2 Causality and Counterfactuals, 26\u003c\/p\u003e \u003cp\u003e2.3 Why Counterfactuals? 32\u003c\/p\u003e \u003cp\u003e2.4 Causal Effects, 33\u003c\/p\u003e \u003cp\u003e2.5 Empirical Effects, 38\u003c\/p\u003e \u003cp\u003eGuidepost 2, 46\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Estimating Causal Effects 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 External Validity, 48\u003c\/p\u003e \u003cp\u003e3.2 Measures of Empirical Effects, 50\u003c\/p\u003e \u003cp\u003e3.3 Difference of Means, 52\u003c\/p\u003e \u003cp\u003e3.4 Risk Difference and Risk Ratio, 55\u003c\/p\u003e \u003cp\u003e3.5 Potential Outcomes, 57\u003c\/p\u003e \u003cp\u003e3.6 Time-Dependent Outcomes, 60\u003c\/p\u003e \u003cp\u003e3.7 Intermediate Variables, 63\u003c\/p\u003e \u003cp\u003e3.8 Measurement of Exposure, 64\u003c\/p\u003e \u003cp\u003e3.9 Measurement of the Outcome Value, 68\u003c\/p\u003e \u003cp\u003e3.10 Confounding Bias, 70\u003c\/p\u003e \u003cp\u003eGuidepost 3, 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Varieties of Bias 72\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Research Designs and Bias, 73\u003c\/p\u003e \u003cp\u003e4.2 Bias in Biomedical Research, 81\u003c\/p\u003e \u003cp\u003e4.3 Bias in Social Science Research, 85\u003c\/p\u003e \u003cp\u003e4.4 Sources of Bias: A Proposed Taxonomy, 90\u003c\/p\u003e \u003cp\u003eGuidepost 4, 92\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Selection Bias 93\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Selection Processes and Bias, 93\u003c\/p\u003e \u003cp\u003e5.2 Traditional Selection Model: Dichotomous Outcome, 100\u003c\/p\u003e \u003cp\u003e5.3 Causal Selection Model: Dichotomous Outcome, 102\u003c\/p\u003e \u003cp\u003e5.4 Randomized Experiments, 104\u003c\/p\u003e \u003cp\u003e5.5 Observational Cohort Studies, 108\u003c\/p\u003e \u003cp\u003e5.6 Traditional Selection Model: Numerical Outcome, 111\u003c\/p\u003e \u003cp\u003e5.7 Causal Selection Model: Numerical Outcome, 114\u003c\/p\u003e \u003cp\u003eGuidepost 5, 121\u003c\/p\u003e \u003cp\u003eAppendix, 122\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Confounding: An Enigma? 126\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 What is the Real Problem? 127\u003c\/p\u003e \u003cp\u003e6.2 Confounding and Extraneous Causes, 127\u003c\/p\u003e \u003cp\u003e6.3 Confounding and Statistical Control, 131\u003c\/p\u003e \u003cp\u003e6.4 Confounding and Comparability, 137\u003c\/p\u003e \u003cp\u003e6.5 Confounding and the Assignment Mechanism, 139\u003c\/p\u003e \u003cp\u003e6.6 Confounding and Model Specifi cation, 141\u003c\/p\u003e \u003cp\u003eGuidepost 6, 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Confounding: Essence, Correction, and Detection 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Essence: The Nature of Confounding, 146\u003c\/p\u003e \u003cp\u003e7.2 Correction: Statistical Control for Confounding, 172\u003c\/p\u003e \u003cp\u003e7.3 Detection: Adequacy of Statistical Adjustment, 180\u003c\/p\u003e \u003cp\u003eGuidepost 7, 191\u003c\/p\u003e \u003cp\u003eAppendix, 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Intermediate Causal Factors 195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Direct and Indirect Effects, 195\u003c\/p\u003e \u003cp\u003e8.2 Principal Stratifi cation, 200\u003c\/p\u003e \u003cp\u003e8.3 Noncompliance, 209\u003c\/p\u003e \u003cp\u003e8.4 Attrition, 214\u003c\/p\u003e \u003cp\u003eGuidepost 8, 215\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Information Bias 217\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Basic Concepts, 218\u003c\/p\u003e \u003cp\u003e9.2 Classical Measurement Model: Dichotomous Outcome, 223\u003c\/p\u003e \u003cp\u003e9.3 Causal Measurement Model: Dichotomous Outcome, 230\u003c\/p\u003e \u003cp\u003e9.4 Classical Measurement Model: Numerical Outcome, 239\u003c\/p\u003e \u003cp\u003e9.5 Causal Measurement Model: Numerical Outcome, 242\u003c\/p\u003e \u003cp\u003e9.6 Covariates Measured with Error, 246\u003c\/p\u003e \u003cp\u003eGuidepost 9, 250\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Sources of Bias 252\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Sampling, 254\u003c\/p\u003e \u003cp\u003e10.2 Assignment, 260\u003c\/p\u003e \u003cp\u003e10.3 Adherence, 266\u003c\/p\u003e \u003cp\u003e10.4 Exposure Ascertainment, 269\u003c\/p\u003e \u003cp\u003e10.5 Outcome Measurement, 273\u003c\/p\u003e \u003cp\u003eGuidepost 10, 277\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Contending with Bias 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Conventional Solutions, 280\u003c\/p\u003e \u003cp\u003e11.2 Standard Statistical Paradigm, 286\u003c\/p\u003e \u003cp\u003e11.3 Toward a Broader Perspective, 288\u003c\/p\u003e \u003cp\u003e11.4 Real-World Bias Revisited, 293\u003c\/p\u003e \u003cp\u003e11.5 Statistics and Causation, 303\u003c\/p\u003e \u003cp\u003eGlossary 309\u003c\/p\u003e \u003cp\u003eBibliography 321\u003c\/p\u003e \u003cp\u003eIndex 340\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402313310551,"sku":"9780470286395","price":98.96,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470286395.jpg?v=1730480035","url":"https:\/\/bookcurl.com\/products\/bias-and-causation-9780470286395","provider":"Book Curl","version":"1.0","type":"link"}